Yi Shui

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About

Yi Shui is a leading researcher in intelligent control systems, with a primary focus on autonomous mobile robotics and advanced fuzzy neural network methodologies. His most notable contribution is the development of a data-driven generalized predictive control (GPC) method for car-like mobile robots, which leverages an interval type-2 T-S fuzzy neural network (IT2TSFNN) to robustly handle nonlinear uncertainties and external disturbances in real-world driving environments. This seminal work, published in 2021 and earning 24 citations, addresses critical challenges in robot motion control by enabling adaptive, model-free predictive strategies that improve stability and trajectory tracking under unpredictable conditions. Shui’s research bridges theoretical advances in fuzzy logic and neural networks with practical applications in autonomous navigation, offering a scalable framework for enhancing the reliability of mobile robots in complex, dynamic settings. His work is particularly influential among engineers and researchers developing next-generation autonomous systems, as it provides a data-efficient alternative to traditional model-based control. With a growing citation impact, Yi Shui continues to shape the field of intelligent robotics, driving innovation in robust, real-time control solutions for autonomous vehicles and mobile platforms.